Econometric Methods

The bread and butter of econometrics are the statistical tools of regression and time series analysis. This is the fourth edition of a highly respected and widely used text on econometric methods. The authors cover regression, correlation and least squares in Chapter 1, starting with the simplest linear regression involving a single regressor variable. This allows for an easy introduction to the basic concepts that provide the foundation for what is to come. Chapter 2 introduces the idea of using time as regressor variable. This is a natural lead-in to the more sophisticated time series models of later chapters. It presents important econometric concepts such as elasticity. It also provides some probability theory and time series theory. Multiple linear regression is then introduced in Chapter 3 along with the important concepts of partial correlation, the Gauss-Markov theorem and variable selection criteria. Also, parameter restrictions are considered in Chapter 3. Chapter 4 includes diagnostic checking of models and the trick of introducing dummy variables into the model to handle dichotomous and categorical variables. The material becomes more difficult and there is an increase in the mathematical sophistication in Chapter 5. More realistic econometric models enter the discussion and the techniques of maximum likelihood, generalized least squares and Lagrange Multipliers are needed. Instrumental variables are introduced to handle such problems as the error in variables model. The technique of two stage least squares is also introduced here. Basic time series ideas and theory were introduced in Chapter 2 but first really get exploited in Chapter 6 where the concepts of heteroscadasticity and autocorrelation are introduced. Formal univariate time domain analysis of time series including the ARIMA models and trending methods are covered in Chapter 7. More complications and advanced theory are in Chapter 8. In Chapter 9, the subject of simultaneous equations is introduced. Generalized Method of Moment methods are presented in Chapter 10 as a reasonable and simple estimation approach that is valid in large samples. Freedman, Navidi, Peters among others have pointed out that the estimators of standard error for parameters in many of the standard econometric methods depend on asymptotic theory and often are very poor for practical problem sizes. They have shown that bootstrap methods can provide much better estimates. It is therefore nice to see that these authors recognize the importance of these resampling methods They devote a full chapter to them. Chapter 11 "A Smorgasbord of Computationally Intensive Methods" covers such resampling techniques as permutation tests, the bootstrap ("nonparametric")and the parametric bootstrap and other computer-intensive methods such as nonparametric density estimation and regression. Other problems that are unique to econometrics are covered in Chapters 12 and 13. Also included are appendices on matrix algebr

Given my relatively weak background in econometrics and statistics, I was afraid I wouldn't be able to understand my graduate econometrics class. However, ever since I started reading from this book, I have managed to follow what my teacher is saying. The steps on how the equations are derived are explained, but without making it too easy for the reader. This textbook is a great help. No wonder this has been around for some time. Nevertheless, I was hoping that there's an answer key for the problems.

After trying many books as a PhD student at Harvard, thisis one of the books I have finally settled on as the best forunderstanding the first year graduate-level fundamentals in econometrics. Just at the right level - keeps econometricsunderstandable without trivializing it or filling up needlesspages.

During my undergraduate lectures my teachers recommended widely the book wrote by Gujarati's. I Think "Econometric Methods" would have been a wiser choice. "Econometric Methods" is more modern than Gujarati's and more understandable than Maddala's book; the book offers to the undergraduate student a very comprehensive array of topics, explained with the simplest mathematics. You won't get lost in large and illegible demonstrations. I particularly enjoyed the treatment of the autoregressive relationships: quite an improvement when compared with most of the introductory level econometric manuals. There is everything in this book, even an excellent introduction to non-parametric estimations; applied econometrists will find all modern tests and will be able to understand them fairly well and use them after a short reading, without having to spend a whole afternoon; the explanation of White variance matrix estimator is extremely intuitive. On the drawbacks I can only mention the short section referring to bootstrap, which is somehow too simplistic and readers won't really understand the subject. After using this manual, you'll feel smart, because you'll understand everything. Being one of the rare books really deserving the adjective "introductory", "Econometric Methods leads you fluidly and intuitively through the topics. A modern array of topics selection and an excellent presentation makes this manual stand out of the crowd.

I guess this is the most complete Econometric Textbook for Undergraduated Students. Its virtue is to give a COMPLETE OVERVIEW of the most important and useful estimation, testing and diagnostic procedures, in the context of TIME SERIES, CROSS SECTION and PANEL DATA ANALYSIS, through a well-written and accessible book. It provides you not only a very good theoretical background, but many empirical applications that you can replicate using the data diskette that comes with the book.A complete reading of this book would give you a very strontg knowledge of Modern Econometrics: you can learn what an applied macro or microeconomist would need and use. This is really a great "jump" from the last edition of Econometric Methods by J. Johnston (1984)(the one I use when I was an undergraduated student). Now, I use this book to teach an undergarduated course of Econometrics at Catholic University (PUCP). My students use it a lot and also think its WONDERFUL!